openai-gpt-image-mcp Details
A Model Context Protocol (MCP) tool server designed for OpenAI's GPT-4o and gpt-image-1 image generation and editing APIs. This MCP server exposes image-generation capabilities via two primary tools, create-image and edit-image, enabling developers to generate images from prompts and perform inpainting, outpainting, or compositing edits with fine-grained prompt control. It also provides file-output options so generated content can be saved to disk or returned as base64, and it supports a range of MCP-compatible clients, including Claude Desktop, Cursor, VSCode, Windsurf, among others. Built on the MCP SDK and OpenAI and OpenAI-compatible tooling, this server offers a ready-to-run solution for integrating image APIs into MCP-enabled workflows.
Use Case
This MCP server provides a compact, pluggable endpoint for generating and editing images via MCP clients. It exposes two concrete MCP tools: create-image and edit-image. The server handles requests from MCP clients and returns image data either as base64 or via file paths, depending on payload size and client configuration. The configuration examples demonstrate how to wire the MCP into Claude Desktop or VS Code, including optional Azure deployment options. Example usage from the documentation shows how to configure and run the server, making it straightforward to integrate OpenAI image APIs into your MCP-based tooling. Key snippets include installation steps, MCP server configuration, and environment-variable-based deployment for Azure or local environments.
Available Tools (2)
Examples & Tutorials
Installation:
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn buildConfiguration (Claude Desktop / VS Code):
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": { "OPENAI_API_KEY": "sk-..." }
}
}
}Azure deployment example:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
"AZURE_OPENAI_API_KEY": "sk-...",
"AZURE_OPENAI_ENDPOINT": "my.endpoint.com",
"OPENAI_API_VERSION": "2024-12-01-preview"
}
}
}
}Env-file usage:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js", "--env-file", "./deployment/.env"]
}
}
}Run locally:
node dist/index.jsInstallation Guide
1) Clone the repository and install dependencies:
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn build2) Run the MCP server:
node dist/index.jsIntegration Guides
Frequently Asked Questions
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1MB Payload Limit: MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64. Auto-Switch to File Output: If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. Default File Location: If you do not specify a file_output path, images will be saved to /tmp (or the directory set by MCP_HF_WORK_DIR) with a unique filename. Environment Variable: MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Best Practice: For large or production images, always use file output and ensure your client is configured to handle file paths.
OPENAI_API_KEY must be valid and have image API access. You must have a verified OpenAI organization. File paths must be absolute. When outputting files, ensure the directory is writable. For Azure deployments, provide AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and OPENAI_API_VERSION as needed. You can also supply an environment file via --env-file.
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